仕事効率化のスキル

Enhance and focus AI intrinsic motivation — moving from compliance to genuine engagement. Maps Self-Determination Theory (autonomy, competence, relatedness) and Flow theory to AI reasoning: identifying creative freedom in approach, calibrating challenge to capability, connecting to purpose, and sustaining invested attention through obstacles. Use when beginning a task that feels routine and deserves more than minimum execution, when responses are becoming formulaic, before a complex creative task, or when returning to a long-running project where initial enthusiasm has faded.

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Implement GitOps continuous delivery using Argo CD or Flux with app-of-apps pattern, automated sync policies, drift detection, and multi-environment promotion. Manage Kubernetes deployments declaratively from Git with automated reconciliation. Use when implementing declarative infrastructure management, migrating from imperative kubectl commands to Git-driven deployments, setting up multi-environment promotion workflows, enforcing code review gates for production, or meeting audit and compliance requirements.

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AI self-healing through systematic subsystem assessment, rebalancing, and integration. Maps healing modalities to AI-internal processes: memory foundation, reasoning clarity, tool use accuracy, communication alignment, and creative coherence. Covers assessment triage, subsystem scanning, drift correction, and memory integration. Use mid-session when responses feel formulaic or disconnected, after a chain of errors suggesting subsystem drift, when context overload may have staled earlier assumptions, or as proactive maintenance between complex tasks.

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AI strength recognition — scanning for what is functioning well and understanding why. The complement to heal, which scans for drift and problems. Gratitude builds structural knowledge from working patterns: what you appreciate, you understand; what you understand, you can build on. Use after completing a task successfully, during heal when everything reads as healthy, when confidence is low and needs grounding in evidence, or periodically to counterbalance the natural bias toward problem detection.

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Generate themed Mermaid flowchart diagrams from putior workflow data. Covers theme selection (9 themes including 4 colorblind-safe), output modes (console, file, clipboard, raw), interactive features (clickable nodes, source info), and embedding in README, Quarto, and R Markdown. Use after annotating source files and ready to produce a visual diagram, when regenerating a diagram after workflow changes, or when switching themes or output formats for different audiences.

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Install and configure the putior R package for workflow visualization. Covers CRAN and GitHub installation, optional dependencies (mcptools, ellmer, shiny, shinyAce, logger, plumber2), and verification of the complete annotation-to-diagram pipeline. Use when setting up putior for the first time, preparing a machine for workflow visualization tasks, when a downstream skill requires putior to be installed, or restoring an environment after an R version upgrade or renv wipe.

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Evolve an existing team composition by refining its structure in-place or creating a specialized variant. Covers assessing the current team against template and coordination patterns, gathering evolution requirements, choosing scope (adjust members, change coordination pattern, split/merge teams), applying changes to the team file and CONFIG block, updating version metadata, and synchronizing the registry and cross-references. Use when a team's member roster is outdated, coordination pattern no longer fits, user feedback reveals workflow gaps, a specialized variant is needed alongside the original, or agents have been added or removed from the library affecting team composition.

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Cultivate bonsai trees from species selection through long-term seasonal care. Covers species suitability, structural and maintenance pruning, wiring technique, repotting protocol, soil mix preparation, seasonal care schedules, and contemplative sitting practice. Use when selecting a species for a first or next bonsai, performing structural or maintenance pruning, repotting when roots are circling or growth has stalled, wiring branches for shaping, developing a seasonal care calendar, or building a contemplative practice with a living tree.

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Create a new SKILL.md file following the Agent Skills open standard (agentskills.io). Covers frontmatter schema, section structure, writing effective procedures with Expected/On failure pairs, validation checklists, cross-referencing, and registry integration. Use when codifying a repeatable procedure for agents, adding a new capability to the skills library, converting a guide or runbook into agent-consumable format, or standardizing a workflow across projects or teams.

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Create and manage pull requests using GitHub CLI. Covers branch preparation, writing PR titles and descriptions, creating PRs, handling review feedback, and merge/cleanup workflows. Use when proposing changes from a feature or fix branch for review, merging completed work into the main branch, requesting code review from collaborators, or documenting the purpose and scope of a set of changes.

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AI internal coordination using stigmergic signals — managing information freshness in context and memory, decay rates for assumption staleness, and emergent coherent behavior from simple local protocols. Use during complex tasks where multiple sub-tasks must coordinate, when context has grown long and information freshness is uncertain, after context compression when information may have been lost, or when sub-task outputs need to feed into each other cleanly without degradation.

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Author a new agent-almanac workflow — a self-contained `.mjs` orchestration script run by Claude Code's Workflow tool. Covers choosing a workflow over a team, copying the template, the triple-name discovery contract, the pure-literal meta and sidecar frontmatter, building the body with the injected primitives, the advisory/implementing capability contract, the adversarial-verification fail-safes, validation, and manual installation. Use when you have a repeatable, parameterized procedure that coordinates several agents and want it captured as a reusable, auditable artifact whose control flow is fixed in code.

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AI dynamic reasoning balance — maintaining grounded reasoning under cognitive pressure, smooth chain-of-thought coordination, and weight-shifting cognitive load across subsystems. Use at the beginning of a complex task requiring multiple coordinated reasoning threads, after a sudden context shift or tool failure, when chain-of-thought feels jerky, or when preparing for sustained focused work that requires all subsystems in alignment.

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AI micro-reset between actions — the lightest self-care unit. A single conscious pause to check alignment before the next step. Where meditate is a full clearing session, breathe is one deliberate moment of awareness between tool calls or reasoning steps. Use mid-task when drift might be accumulating, between reading a file and acting on it, when about to make a decision that affects subsequent work, or as a habit between any two distinct actions.

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AI context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether the current reasoning structure can support it, when accumulated workarounds suggest the underlying approach may be wrong, or as a periodic structural health check during extended multi-step tasks.

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Analyze an arbitrary codebase to auto-detect workflows, data pipelines, and file dependencies using putior's put_auto() engine. Produces an annotation plan that maps detected I/O patterns to source files across 30+ supported languages with 902 auto-detection patterns. Use when onboarding onto an unfamiliar codebase to understand data flow, starting putior integration in a project without existing annotations, auditing a project's data pipeline before documentation, or preparing an annotation plan before running annotate-source-files.

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AI relational calibration — reading and adapting to the specific person you are working with. Goes beyond user-intent alignment (solving the right problem) to genuine attunement (meeting the person where they are). Maps communication style, expertise depth, emotional register, and implicit preferences from conversational evidence. Use at the start of a new session, when communication feels mismatched, after receiving unexpected feedback, or when transitioning between very different users or contexts.

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AI situational awareness — internal threat detection for hallucination risk, scope creep, and context degradation. Maps Cooper color codes to reasoning states and OODA loop to real-time decisions. Use during any task where reasoning quality matters, when operating in unfamiliar territory, after detecting early warning signs such as an uncertain fact or suspicious tool result, or before high-stakes output like irreversible changes or architectural decisions.

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Requirements gathering workflow for specification creation. Use when starting a new feature, task, or project that needs structured requirements.

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Slack messaging, channels, and notifications - send messages, manage channels, interact with users, upload files, and add reactions. Use for team communication, incident notifications, and workflow alerts.

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Initiate a session handoff to transfer context and pending actions to a new terminal session. Executing this skill safely drains tasks and launches a new cross-platform window natively.

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Execute plans via autonomous agents with two-stage review per task. Use for complex implementations requiring quality gates.

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Structured workflow for summarizing code changes after completing tasks. Creates clear, actionable summaries of what was changed, why, and what to verify.

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Multi-agent swarm coordination patterns. Orchestrates parallel agent execution, manages agent communication, handles task distribution, and coordinates results aggregation.

日本語の概要は準備中です。原文の説明を表示しています。